Forecasting Methods

All businesses are confronted with the general problem of having to make decisions under conditions of uncertainty. Management must understand the nature of demand and competition in order to develop realistic business plans, determine a strategic vision for the organization, and determine technology and infrastructure needs. To address these challenges, forecasting is used. According to Makridakis (1989), forecasting future events can be characterized as the search for answers to one or more of the following questions: XWhat new economic, technical, or sociological forces is the organization likely to face in both the near and long term? XWhen might these forces impact the firm¡¦s objective environment? XWho is likely to be first to adapt to each competitive challenge? XHow much change should the firm anticipate both in the short run and the long run? In this paper, I will provide an overview of forecasting methods and compare and contrast these various methods. The paper will then focus on how Mattel, one of the nations largest toy manufacturers, uses demand forecasting under conditions of uncertainty ¡V most specifically those relating to the pattern and rate at which customers demand products. What is Forecasting?

In Operations Management, demand forecasting is defined as ¡§the business process that attempts to estimate sales and the use of products so that they can be purchased, stocked, or manufactured in appropriate quantities in advance to support the firm¡¦s value adding activities.¡¨(Ross, 1995). Forecasting is a process that transforms historical time-series data and/or qualitative assessments into statements about future events. This process can produce either qualitative or subjective projections. Note that no forecasting process can consistently provide perfect forecasts. Any forecast that perfectly estimates subsequent events should raise cause for alarm, as this is probably indicative of improprieties such as ¡§cooking the books¡¦ or reporting performance data that shows conformance with plans versus actual events (Makridakis, 1989). Forecasting Methods

There are four basic types of forecasting methods: qualitative, time series analysis, causal relationships, and simulation. Qualitative TechniquesQualitative techniques are subjective or judgmental and based on estimates and opinions (Chase, 2005). These forecasts reflect people¡¦s judgments or opinions and suggest likely conditions, such as people¡¦s opinion about whether it will rain today. These forecasts are preferred when there is a desire to engage individuals within the organization with a key business process. A potential pitfall of this technique is that some individuals base their judgments of future events on historical data, which may not provide relevant demand patterns that are stable enough to warrant their use to forecast future events. Additionally, emerging demand patterns may be too unstable for a numeric approach. Consequently, intimate knowledge of the market should be the data source of choice. There are numerous qualitative approaches to demand forecasting, following are some of the more common approaches: XGrass-Roots Forecasting seeks input from people at the level of the organization that gives them the best contact with the event under study (Chase, 2005). This technique may consist of conducting a marketing study of sales representatives for their readings on current market conditions. The potential fault with this tool is that it is subject to the short-term perspectives of the sources. The source of the data may be unduly influenced by recent events. For example, a sales person who has had a good day may provide an overly-optimistic forecast for the future that does not accurately represent market conditions on the whole. XHistorical Analogy: Forecasting based on historical analogy explores the possibility that past events can provide insights into the prediction of future related events....

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...The objective of this assignment is to investigate different business forecastingmethods, and demonstrate the benefits of their use for a specific organization. We have learned that demand forecasting invokes the processes of determining exactly what service/products are needed, in what quantity, and in what amount of time. Organizations that are able to implement effective forecasting will be better equipped to find the balance between managing demand for a product/service and the capacity to meet this demand. The ability of optimizing this unique balance enables an organization to use this as a competitive advantage over their competitors
There are a variety of forecasting models to choose from and organizations should first decide which type of business decision is being made. This initial determination will allow managers to decide which forecastingmethods are appropriate or not given the period of time allotted. There are four basic families that describe distinct demand forecasting techniques which include the qualitative, time series analysis, causal, and simulation models. We have learned that it is important to keep options open to apply different models - the one most readily available or commonly used may not be the most appropriate, and choosing the wrong one can cost a larger organization millions.
Qualitative Models
The qualitative...

...Forecasting Methodology
Forecasting is an integral part in planning the financial future of any business and allows the company to consider probabilities of current and future trends using existing data and facts. Forecasts are vital to every business organization and for every significant management decision. Forecasting, according to Armstrong (2001), is the basis of corporate long-run planning. Many times, this unique approach is used not only to provide a baseline, but also to offer a prediction into the corporation's future. In the functional areas of finance and accounting, forecasts provide the basis for budgetary planning and cost control. Marketing relies on sales forecasting to plan new products, compensate sales personnel, and make other key decisions. Production and operations personnel use forecasts to make periodic decisions involving process selection, capacity planning, and facility layout, as well as for continual decisions about production planning, scheduling, and inventory. Planning problems, whether dealing with services or merchandise, can cause any manager headaches easily solved by forecasting. It is important that any manager realizes that the past is a key to the future. Although no long-term plan is perfect, using the correct forecasting tool, along with continual evaluation, allows the manager to review and update corporate financial plans.
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...Business forecastingmethods
Rob J Hyndman November 8, 2009 1 Forecasting, planning and goals
Forecasting is a common statistical task in business, where it helps inform decisions about scheduling of production, transportation and personnel, and provides a guide to long-term strategic planning. However, business forecasting is often done poorly and is frequently confused with planning and goals. They are three diﬀerent things. Forecasting is about predicting the future as accurately as possible, given all the information available including historical data and knowledge of any future events that might impact the forecasts. Goals are what you would like to happen. Goals should be linked to forecasts and plans, but this does not always occur. Too often, goals are set without any plan for how to achieve them, and no forecasts for whether they are realistic. Planning is a response to forecasts and goals. Planning involves determining the appropriate actions that are required to make your forecasts match your goals. Forecasting should be an integral part of the decision-making activities of management, as it can play an important role in many areas of a company. Modern organizations require short-, medium- and long-term forecasts, depending on the speciﬁc application. Short-term forecasts are needed for scheduling of personnel, production and transportation. As part of the...

...﻿CHAPTER 4: FORECASTING
TRUE/FALSE
1. Tupperware only uses both qualitative and quantitative forecasting techniques, culminating in a final forecast that is the consensus of all participating managers.
False (Global company profile: Tupperware Corporation, moderate)
2. The forecasting time horizon and the forecasting techniques used tend to vary over the life cycle of a product.
True (What is forecasting? moderate)
3. Sales forecasts are an input to financial planning, while demand forecasts impact human resource decisions.
True (Types of forecasts, moderate)
4. Forecasts of individual products tend to be more accurate than forecasts of product families.
False (Seven steps in the forecasting system, moderate)
5. Most forecasting techniques assume that there is some underlying stability in the system.
True (Seven steps in the forecasting system, moderate)
6. The sales force composite forecastingmethod relies on salespersons’ estimates of expected sales.
True (Forecasting approaches, easy)
7. A time-series model uses a series of past data points to make the forecast.
True (Forecasting approaches, moderate)
8. The quarterly "make meeting" of Lexus dealers is an example of a sales force composite forecast.
True (Forecasting approaches, easy)
9. Cycles and...

...Business Forecasting Coursework
Introduction
The data of this coursework are business investment in the quarterly series in the manufacturing sector from 1994 to the second quarter of 2008 in UK. In the coursework, firstly analyze the former 50 data to forecast the latter 8 ones and then compare with the real data to see if the forecasting model is a good fit or not. As adopting two different approaches to make the forecasting work, including regression with Dummy Variables method and Box-Jenkins ARIMA method, according to the results, relative comparisons will be made to demonstrate which one is a better choice for this certain question. Then discuss the underlying assumptions of the chosen model and evaluate whether it is sensitive to these assumptions. All the analyses are based on the SPSS software and the graphs are from the output.
Part 1. Examine the data
To apply certain model to forecast future value, find out the seasonal component, trends and cycles component is the basic job. There are two approaches to examine the data: see the time series plot (chart 1) or use ACF (chart 2).
Chart 1 Plot of the data
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Chart 2 ACF/PACF of the data
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From both Chart 1 and Chart 2, the drawing conclusion is that the data has trend-cycle and seasonal components. Firstly, although there is no general upward trend and downward trend, clearly there is a cycle component: the data...

...QUALITATIVE FORECASTINGMETHODS
Qualitative forecastingmethods are based on educated opinions of appropriate persons
1. Delphi method: forecast is developed by a panel of experts who anonymously answer a series of questions; responses are fed back to panel members who then may change their original responses
a- very time consuming and expensive
b- new groupware makes this process much more feasible
2. Market research: panels, questionnaires, test markets, surveys, etc.
3. Product life-cycle analogy: forecasts based on life-cycles of similar products, services, or processes
4. Expert judgement: by management, sales force, or other knowledgeable persons
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QUANTITATIVE FORECASTINGMETHODS
TIME SERIES FORECASTINGMETHODS
Time series forecastingmethods are based on analysis of historical data (time series: a set of observations measured at successive times or over successive periods). They make the assumption that past patterns in data can be used to forecast future data points.
1. Moving averages (simple moving average, weighted moving average): forecast is based...

...CHAPTER?
DEMAND FORECASTING IN A
S UPPLY CHAIN
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Learning Objectives
After reading this chapter, you will be able to:
1. Understand the role of forecasting for both an enterprise and a supply chain. 2. Identify the components of a demand forecast. 3. Forecast demand in a supply chain given historical demand data using time-series methodologies. 4. Analyze demand forecasts to estimate forecast error.
F
7.1
orecasts of future demand are essential for making supply chain decisions. In this chapter, we explain how historical demand information can be used to forecast future demand and how these forecasts affect the supply chain. We describe several methods to forecast demand and estimate a forecast's accuracy. We then discuss how these methods can be implemented using Microsoft Excel.
THE ROLE OF FORECASTING IN A SUPPLY CHAIN
Demand forecasts form the basis of all supply chain planning. Consider the push/pull view of the supply chain discussed in Chapter 1. All push processes in the supply chain are performed in anticipation of customer demand, whereas all pull processes are per­ formed in response to customer demand. For push processes, a manager must plan the level of activity, be it production, transportation, or any other planned activity. For pull processes, a manager must plan the level of available capacity and inventory but not the actual amount to be executed. In both...

...Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgemental methods. Usage can differ between areas of application: for example, in hydrology, the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period.
Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible.
Although quantitative analysis can be very precise, it is not always appropriate. Some experts in the field of forecasting have advised against the use of mean square error to compare forecastingmethods.
Forecasting involves the use of information at hand to make statements about the likely course of future events. In technical terms,...